Armada raises $230M at $2B to bring modular AI data centers to the 70% of the world without hyperscale infrastructure

Speaker 2: we have our first guest of the show, Dan Wright from Armada in the waiting room. Let's bring him in to the TVP on Ultradome. Dan, how are doing? There he is. I'm doing great. How are guys doing? We're doing fantastically. Welcome to the show. Welcome back. What is new in your Walk us through the latest. And maybe just, since it's been a while, sort of reintroduce the the shape of the company, the mission, and, where you guys have gone in the last year.

Speaker 12: Yeah. So Armada is the hyperscaler for the edge. We build the infrastructure for the 70% of the world that doesn't have the big hyperscale data centers today. The mission of the company is to bridge the digital divide and make sure that we have AI everywhere, wherever we need it. But first, got to have the infrastructure there. And so that's what we're doing. We've been very busy. We just raised earlier this year $230,000,000 at a $2,000,000,000 pre. We launched Galleon Forge One which is a factory with our partner Johnson Controls in Gilbert, Arizona, where we're now continuously manufacturing

Speaker 2: these modular AI data centers called Galleons that we built. Yeah. How is power usually solved for when you deploy these?

Speaker 12: Yeah. So a lot of times we're deploying them where there's already stranded power. Sure. A good example of this is a couple of weeks ago, I was in New York and we did an event at the New York Stock Exchange where we were sort of fast following on Jensen's announcement that compute is now an asset class. It's a new asset class. The New York Stock Exchange is actually making that now like something that you can invest in the same way that you can invest in electricity or other types of commodities. And we were there with our customer in Norway called Fossifal that has tons of distributed sites all over Norway. I'm actually going be in Norway with them next week, but they also have them in Finland and in Sweden, and it is largely renewable energy. So it's 99% hydroelectricity. They can just plug in our AI factories and then scale up quickly. And one of the things that makes that easier is that we've recently announced some larger form factors. Last year, announced Leviathan, which is two megawatts per unit. And when I was in New York, we announced Orion, which is our newest form factor. That's 10 megawatts per unit. When I was, you know, launching Leviathan, everybody said that's great, but how do we scale up even faster? So now Armodic incredibly say, wherever you have power, we can plug in and we're the fastest from zero to 200 megawatts anywhere in the world.

Speaker 1: Talk about why, for example, one of these companies needs to have that compute actually at the edge and why it matters across different industries.

Speaker 12: So one is latency. And this comes up in a lot of conversations. I'll give you another real world example. We work with the state of Alaska and they don't have the big hyperscale data centers there. So the first data centers deployed were ours. We did this last year and we're still working closely with them and scaling up with them. They had twenty eight hours of latency to process data from drones for emergency response, for avalanches and floods. We also deployed last year with the Navy in the middle of the ocean, very similar types of use cases where if you have a large distance between the source of the data where the data is being generated and then where it's being processed, the data sort of becomes worthless because avalanches and floods and threats and battlefield scenarios, they don't wait days or even minutes. You have to be able to use the data in real time. Another big driver of this is sovereignty. The shorthand for our value prop is the three S's speed, scale, and sovereignty. And the sovereignty piece is really important. There's this global trend that's going on around sovereign AI. Everybody wants to be able to take the latest models, but they wanna be able to fine tune them to really sensitive data sets that they wouldn't send to the cloud. And then, you know, have like a sovereign AI infrastructure that they actually own, and that's what Armada enables.

Speaker 2: So, yeah, speed of delivery. Yeah. That makes a lot of sense. Is hydroelectricity under discussed right now? Is there an opportunity there in America or abroad? I mean, we talk a lot about solar where it feels like there there aren't as many, like, big winners yet or big, like, hot startups that exist in nuclear. That's very exciting. I've you know, we've heard about wind, but no one is really talking about, like, let's just do another Hoover Dam or something. Is that possible? Are you optimistic about?

Speaker 12: I think so. I think I think it should be talked about more. And I think in general, stranded energy should be talked about more. We're doing these projects all over the world as an example. You know, the the Niners are playing in Australia this week and we're doing an event there with another partner called WindDC that we work with there and they've got a huge amount of stranded wind and solar energy. Australia is big in wind and solar. Norway is big in hydroelectricity, but this happening all over the world. There's these stranded pockets of energy, and they last year had to curtail 7.2 terawatt hours of energy in Australia because the grid's completely overloaded, but they have this stranded power that we're just bringing the infrastructure directly to and then you can scale up into the hundreds of megawatts. Then ultimately, same with VOSFOL, they'll they'll scale to over a gigawatt over the next few years, and we can scale with that. Yeah. How flexible do you want to be around,

Speaker 2: like, the actual chips that go into the systems? Because when I hear latency, I feel like Cerebras, Croc, these, like, faster systems. There's Talos, which I think AMD just bought, where you're baking the weights on. And you can actually get to an inference speed for certain AI workloads that would actually benefit from saving one hundred milliseconds, whereas if you're putting a bunch of NVL 72s in a data center, you're going to be waiting while it's cooking. Right. And the last mile is going to be negligible.

Speaker 12: Yes. So again, it's all about speed, scale, and sovereignty and then giving the customer the choice. So we say, Okay, well, what workloads are you trying to run? Yeah. And then based on that, you can sort of right size the infrastructure to the actual need and where they expect it to go. And a lot of what our customers want to do is they want to try different things. Want to try, you know, the GBs, but now they're looking at the Vera Rubins. They're looking at things from other, you know, chip companies as well. And so the nice thing about our galleons, again, we're manufacturing these. It's not like construction. And so as you want to try different things, we can just make, you know, quick iterations on the design and ship them out. So they can try different things and then what they like, just order more of and we scale up with the demand versus with the traditional data center. Yeah. The downside is not just as we were talking about the power, you got to figure out the power situation. We take advantage of the power that's already there, But what we also allow them to do is scale up with demands that they don't overbuild or build the wrong thing.

Speaker 1: How are you thinking about, you know, staying aligned to, I guess, like why and how the company started and the focus around this edge compute versus natural pull from the market where if somebody comes to you and says, like, okay, now I want the one gigawatt data center and we think you guys can can do it. Like, just running the numbers on that and and resource allocation, I imagine there's a natural pull to go bigger and bigger when and anyways, think that's kind of a Yeah. Was actually a good problem to have, but

Speaker 12: will be interesting. It was actually super simple for us because one of our company values is heal the customer's pain first. We're like obsessed with customers and what their pains are and how do we solve those. And so the kind of evolution of our Galleon product line has all come out of conversations with existing customers where they've said, okay, I want to use your edge galleons, the smaller ones, say sub a megawatt for inference. But then they came to us and said, also want to do fine tuning of all these models, latest open source models, closed models, my own models on these proprietary data sets without sending them to the cloud. And so what we enable is Sovereign AI factories where they can do both. They can take the latest models, open source models, you probably saw what NVIDIA just did with Hugging Face and with Poolside. They were going see a lot more around open source. They can take models from the OpenAI's and the Anthropics of the world. We can help them fine tune those to the sovereign data sets and then push them to all the edge nodes to run. Then you can do what's called federated learning. Yeah. Where you're actually fine tuning that model on that data at the site, and then you're using it to improve your core model and then pushing the updated model out to all the sites. Yep. Which has the benefit of it's more cost effective, it's more secure, and it enables you to take advantage of lots of different models, experiment with different things, and see what works.

Speaker 2: Congratulations Well, on the progress. Thank you so much for coming on. Great update. We'll talk to you soon. Great to see you guys. Great to see you, Dan. Congrats on the progress. Let me tell you about Shopify. Shopify is a commerce platform that grows with your business and lets you sell in seconds online, in store, on mobile, on social, and marketplaces,